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        <title>Triad-based Neural Network for Coreference Resolution</title>
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        <namePart type="given">Yuanliang</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>We propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. The system simultaneously accepts three mentions as input, taking mutual dependency and logical constraints of all three mentions into account, and thus makes more accurate predictions than the traditional pairwise approach. Depending on system choices, the affinity scores can be further used in clustering or mention ranking. Our experiments show that a standard hierarchical clustering using the scores produces state-of-art results with MUC and B 3 metrics on the English portion of CoNLL 2012 Shared Task. The model does not rely on many handcrafted features and is easy to train and use. The triads can also be easily extended to polyads of higher orders. To our knowledge, this is the first neural network system to model mutual dependency of more than two members at mention level.</abstract>
    <identifier type="citekey">meng-rumshisky-2018-triad</identifier>
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        <url>https://aclanthology.org/C18-1004</url>
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%0 Conference Proceedings
%T Triad-based Neural Network for Coreference Resolution
%A Meng, Yuanliang
%A Rumshisky, Anna
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F meng-rumshisky-2018-triad
%X We propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. The system simultaneously accepts three mentions as input, taking mutual dependency and logical constraints of all three mentions into account, and thus makes more accurate predictions than the traditional pairwise approach. Depending on system choices, the affinity scores can be further used in clustering or mention ranking. Our experiments show that a standard hierarchical clustering using the scores produces state-of-art results with MUC and B 3 metrics on the English portion of CoNLL 2012 Shared Task. The model does not rely on many handcrafted features and is easy to train and use. The triads can also be easily extended to polyads of higher orders. To our knowledge, this is the first neural network system to model mutual dependency of more than two members at mention level.
%U https://aclanthology.org/C18-1004
%P 35-43

Markdown (Informal)

[Triad-based Neural Network for Coreference Resolution](https://aclanthology.org/C18-1004) (Meng & Rumshisky, COLING 2018)

• Triad-based Neural Network for Coreference Resolution (Meng & Rumshisky, COLING 2018)

ACL

• Yuanliang Meng and Anna Rumshisky. 2018. Triad-based Neural Network for Coreference Resolution. In Proceedings of the 27th International Conference on Computational Linguistics, pages 35–43, Santa Fe, New Mexico, USA. Association for Computational Linguistics.